In industries that require physical manipulation of objects or people, such as material handling, patient handling, manufacturing, or construction, workers often perform a variety of manual tasks such as lifting loads, moving loads from one location to another, pushing and pulling carts or trolleys, complex assembly and manipulation of components using specific motions and using vibration and impact tools. Often these motions require an intense physical effort, and therefore the repetition of these tasks over time can cause fatigue and injury.
Wearable technology has been used extensively in the consumer space to quantify, for example, the number of steps taken, distance traveled, length and quality of sleep and other metrics, but wearable technology has not been able to consistently evaluate safety metrics in the materials handling industry.
Many risks associated with material handling workers exist, including repetitive stress injuries based on extended physical effort over prolonged periods of time.
Current solutions are mostly limited to physical inspection by specialists, since there is a lack of effective tools to predict when lifting posture is incorrect, or when fatigue results in a risky or dangerous change of posture or non-ergonomic lifting techniques when performing tasks. Typically, specialists inspect the workplace and observe tasks, or review video footage provided by the employer. In either case, inspection is typically performed over only a limited period of time, usually 5-60 minutes. Without effective tools, employers (and workers themselves) have difficulty predicting and preventing injury.
Further, while workers are taught correct material handling techniques, such techniques are not tailored to the strengths of a particular worker. Different workers can do a particular task in multiple ways because of varying body types and abilities. Better monitoring of task performance incorporating information about the particular worker involved may allow for customized training techniques.
Further, there is a lack of productivity measuring tools for individual workers, as it is rarely possible to measure in real-time the number and quality of tasks a specific worker is performing including their speed and variation over time. This information could allow managers to optimize productivity or to devise novel forms of incentives based on productivity.
Finally, tasks are typically divided among the workers based partially on physical ability. However, the physical ability to do a specific task is determined based on visual observation without any detailed insights on the actual motion of a worker's body. Quantifying body motion can help supervisors factor such information into task and shift assignments. Therefore, additional information related to the aspects of task performance that increase injury risk can inform the design of a workplace, design of shifts, and assignment of tasks.
Existing systems for analyzing the safety and productivity of material handling tasks by analyzing motion have limited real-world applications due to inherent limitations.
Motion detection based platforms, such as optical systems using complex cameras and sensors, are expensive and are of limited use in a warehouse setting as they require line of sight which is not always possible in crowded warehouse or factory environments.
Electromagnetic based motion sensor systems produce errors when they are close to ferromagnetic materials often present in industrial settings, are expensive and typically require cabling from sensors to processing units, making their continued use impractical in a warehouse setting.
Existing devices provide very limited motion information and are typically bulky and impractical. Existing systems cannot extract adequate information to fully implement risk models, and typically require manual input of risk variables that cannot be measured by the device alone.
Further, in systems where devices are assigned to users for tracking purposes, the devices are typically stored at an employer facility along with devices associated with or assigned to other workers. It is difficult to ensure that a particular user is using the correct device, and that the device is assigned to that worker.
Further, where workers may be tracked, feedback is not always provided, and where feedback is provided, it is not always immediate or provided in a useful form. Further, feedback may not prevent workers from performing dangerous tasks, even where such feedback is received. For example, a worker may receive a warning that it is dangerous to drive a forklift without a helmet, but such a system may not know when the worker is attempting to drive a forklift or prevent the user from doing so if an attempt is detected.
Further, where worker activities may be used to provide feedback, such systems cannot generate insight into longer term injuries, beyond detecting that a single action is potentially dangerous.
Finally, none of the tracking systems described can leverage the tracking information to improve morale by encouraging and incentivizing safe practices or to reduce costs by incentivizing and confirming practices that reduce insurance premiums.
There is a need for a fully automatable system and method that can monitor physical activity of individual workers and evaluate safety and productivity both for individuals and for a workspace as a whole. There is a further need for a platform that can incorporate such evaluations into recommendations for improving the technique of individual workers and physical characteristics of the workplace environment.
Finally, there is a need for such a platform that can leverage data generated to improve morale, ensure safety, and reduce employer costs.